Health information technology (IT) will be used more frequently by healthcare providers to acquire, manage, analyze, and disseminate healthcare information and knowledge for better patient care in the coming years. Computerized Clinical Decision Support (CDS) systems, a part of health IT applications, have been shown to improve the quality, safety, and value of healthcare. However, due to the lack of understanding of the complexities surrounding decision tasks, system design in medicine does not support a higher level of reasoning in decision support. Therefore, to guide the design of high-level reasoning in CDS systems, it is imperative to understand the complex decision-making patterns and the factors that contribute to decision task complexity. Despite substantial prior research on task complexity in other domains, less is known about complex clinical decision tasks. In response to this pressing gap, the objective of our research is to increase the understanding of complexity in clinical decision tasks. Task complexity is well defined in other successful areas of system design including defense, the humanities, engineering, business and the social sciences. We will utilize successful models from other fields for task complexity to understand the factors necessary for innovative design of CDS systems. To achieve this goal, we will characterize the decision-making patterns of clinical experts through clinical incident interviews (Aim 1); identify factors that contribute to complex decision tasks through clinician observations (Aim 2); and utilize these findings to design and evaluate a prototypical CDSS that uses visual and population analytics to improve decision quality (Aim 3). This application responds to the AHRQ SEN (NOT-HS-13-011) with emphasis on the system Design research area. The research will be conducted in the domain of Infectious Diseases (ID) due to its dynamic complexity and importance in the public health domain.